From calling an API to shipping a full RAG-powered application.
Comfort with Python is the entry ticket — most AI tooling and APIs are Python-first.
Learn how language models actually work, including their real limitations — this shapes every design decision afterward.
Structured, reliable prompting is the foundation every other GenAI skill builds on.
Learn chunking, embeddings, and retrieval — the technique that lets an LLM answer questions about your own data accurately.
Extend beyond answering questions into taking multi-step actions through tool-calling and orchestration.
Combine RAG and agent skills into one complete, working application — the portfolio piece that demonstrates real capability.
Every stage above is built into the GenAI & LLM Engineering program's curriculum, labs, and projects.
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